publication . Article . Conference object . Preprint . 2016

Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text

Manish Shrivastava;
  • Published: 02 Nov 2016
Abstract
Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack of a suitable annotated dataset. We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media. In this paper, we introduce learning sub-word level representations in LSTM (Subword-LSTM) architecture instead of character-level or word-level representations. This linguistic prior in...
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free text keywords: Computer Science - Computation and Language
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arXiv.org e-Print Archive
Preprint . 2016
Provider: arXiv.org e-Print Archive
35 references, page 1 of 3

Akshat Bakliwal, Piyush Arora, and Vasudeva Varma. 2012. Hindi subjective lexicon: A lexical resource for hindi polarity classification. In Proceedings of International Conference on Language Resources and Evaluation.

Utsab Barman, Amitava Das, Joachim Wagner, and Jennifer Foster. 2014. Code-mixing: A challenge for language identification in the language of social media. In In Proceedings of the First Workshop on Computational Approaches to Code-Switching.

Irshad Ahmad Bhat, Vandan Mujadia, Aniruddha Tammewar, Riyaz Ahmad Bhat, and Manish Shrivastava. 2015. Iiit-h system submission for fire2014 shared task on transliterated search. In Proceedings of the Forum for Information Retrieval Evaluation, FIRE '14, pages 48-53, New York, NY, USA. ACM.

John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification. In In ACL, pages 187-205.

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching word vectors with subword information. CoRR, abs/1607.04606.

Ziqiang Cao, Furu Wei, Li Dong, Sujian Li, and Ming Zhou. 2015. Ranking with recursive neural networks and its application to multi-document summarization. In AAAI, pages 2153-2159.

Gokul Chittaranjan, Yogarshi Vyas, Kalika Bali, and Monojit Choudhury. 2014. Word-level language identification using crf: Code-switching shared task report of msr india system. In Proceedings of The First Workshop on Computational Approaches to Code Switching, pages 73-79.

Kyunghyun Cho, Bart Van Merrie¨nboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Jacob Cohen. 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1):37-46, April.

Amitava Das and Sivaji Bandyopadhyay. 2010. Sentiwordnet for indian languages. In Proceedings of the Eighth Workshop on Asian Language Resouces.

C´ıcero Nogueira dos Santos and Victor Guimara˜es. 2015. Boosting named entity recognition with neural character embeddings. CoRR, abs/1505.05008.

C´ıcero Nogueira dos Santos and Bianca Zadrozny. 2014. Learning character-level representations for part-ofspeech tagging. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, pages 1818-1826.

Luisia Duran. 1994. Toward a better understanding of code switching and interlanguage in bilinguality: Implications for bilingual instruction. Journal of Educational Issues of Language Minority Students, 14:69-87.

Andrea Esuli and Fabrizio Sebastiani. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC06, pages 417-422.

Ronen Feldman. 2013. Techniques and applications for sentiment analysis. Commun. ACM, 56(4):82-89, April.

35 references, page 1 of 3
Related research
Abstract
Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack of a suitable annotated dataset. We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media. In this paper, we introduce learning sub-word level representations in LSTM (Subword-LSTM) architecture instead of character-level or word-level representations. This linguistic prior in...
Read more
Subjects
free text keywords: Computer Science - Computation and Language
Related Organizations
View more
Download fromView all 3 versions
arXiv.org e-Print Archive
Preprint . 2016
Provider: arXiv.org e-Print Archive
35 references, page 1 of 3

Akshat Bakliwal, Piyush Arora, and Vasudeva Varma. 2012. Hindi subjective lexicon: A lexical resource for hindi polarity classification. In Proceedings of International Conference on Language Resources and Evaluation.

Utsab Barman, Amitava Das, Joachim Wagner, and Jennifer Foster. 2014. Code-mixing: A challenge for language identification in the language of social media. In In Proceedings of the First Workshop on Computational Approaches to Code-Switching.

Irshad Ahmad Bhat, Vandan Mujadia, Aniruddha Tammewar, Riyaz Ahmad Bhat, and Manish Shrivastava. 2015. Iiit-h system submission for fire2014 shared task on transliterated search. In Proceedings of the Forum for Information Retrieval Evaluation, FIRE '14, pages 48-53, New York, NY, USA. ACM.

John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification. In In ACL, pages 187-205.

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching word vectors with subword information. CoRR, abs/1607.04606.

Ziqiang Cao, Furu Wei, Li Dong, Sujian Li, and Ming Zhou. 2015. Ranking with recursive neural networks and its application to multi-document summarization. In AAAI, pages 2153-2159.

Gokul Chittaranjan, Yogarshi Vyas, Kalika Bali, and Monojit Choudhury. 2014. Word-level language identification using crf: Code-switching shared task report of msr india system. In Proceedings of The First Workshop on Computational Approaches to Code Switching, pages 73-79.

Kyunghyun Cho, Bart Van Merrie¨nboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Jacob Cohen. 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1):37-46, April.

Amitava Das and Sivaji Bandyopadhyay. 2010. Sentiwordnet for indian languages. In Proceedings of the Eighth Workshop on Asian Language Resouces.

C´ıcero Nogueira dos Santos and Victor Guimara˜es. 2015. Boosting named entity recognition with neural character embeddings. CoRR, abs/1505.05008.

C´ıcero Nogueira dos Santos and Bianca Zadrozny. 2014. Learning character-level representations for part-ofspeech tagging. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, pages 1818-1826.

Luisia Duran. 1994. Toward a better understanding of code switching and interlanguage in bilinguality: Implications for bilingual instruction. Journal of Educational Issues of Language Minority Students, 14:69-87.

Andrea Esuli and Fabrizio Sebastiani. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC06, pages 417-422.

Ronen Feldman. 2013. Techniques and applications for sentiment analysis. Commun. ACM, 56(4):82-89, April.

35 references, page 1 of 3
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